Critical considerations in the development and interpretation of common risk language
Bibliographic record
Abstract
Existing risk communication procedures are marred by various well-documented problems and inconsistencies. The Council of State Governments' Justice Center (United States) developed a five-level system for risk and needs communication, to standardize these procedures and to provide a common risk language. Introduction of a common language could constitute a dramatic shift in criminal justice processes, with wide-ranging impacts. This article provides a critical review of the system and its suitability for application to various risk assessment functions. Issues discussed include: applicability to specialist and generalist offending behavior, the characteristics of suitable instruments, statistical and conceptual priorities, barriers to precision in language, and conceptual issues related to changes in risk level. A thorough understanding of each of these issues is necessary to apply the system to new contexts and populations, and facilitate straightforward and precise risk communication. Absent further elaboration of the system, many problems with risk communication will persist.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.432 | 0.526 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.013 | 0.070 |
| Scholarly communication | 0.030 | 0.041 |
| Open science | 0.016 | 0.018 |
| Research integrity | 0.012 | 0.033 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".